A Deep Learning-Based Model for Predicting Abnormal Liver Function in Workers in the Automotive Manufacturing Industry

A Cross-Sectional Survey in Chongqing, China

Bibliographic Data

ID15500772
AuthorsLinghao Ni (0000-0001-6434-5778, Chongqing Public Health Medical Center), Fengqiong Chen (0000-0002-2572-3796, Department of Occupational Health and Radiation Health, Chongqing Center for Disease Control and Prevention, Chongqing 400042, China), Ruihong Ran (Department of Occupational Health and Radiation Health, Chongqing Center for Disease Control and Prevention, Chongqing 400042, China), Xiaoping Li (0000-0003-3529-390X, Department of Occupational Health and Radiation Health, Chongqing Center for Disease Control and Prevention, Chongqing 400042, China), Nan Jin (0000-0001-7772-3563, Department of Occupational Health and Radiation Health, Chongqing Center for Disease Control and Prevention, Chongqing 400042, China), Huadong Zhang (0000-0002-5488-5102, Department of Occupational Health and Radiation Health, Chongqing Center for Disease Control and Prevention, Chongqing 400042, China, corresponding author), Bin Peng (0000-0001-5672-2086, Chongqing Public Health Medical Center, corresponding author)
Year2022
Volume19
Issue21
Pages14300-14300
Publication date2022-11-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph192114300
PMID36361178
OpenAlexW4307864081
LanguageEN
References cited27

To identify the influencing factors and develop a predictive model for the risk of abnormal liver function in the automotive manufacturing industry works in Chongqing. Automotive manufacturing workers in Chongqing city surveyed during 2019-2021 were used as the study subjects. Logistic regression analysis was used to identify the influencing factors of abnormal liver function. A restricted cubic spline model was used to further explore the influence of the length of service. Finally, a deep neural network-based model for predicting the risk of abnormal liver function among workers was developed. Of all 6087 study subjects, a total of 1018 (16.7%) cases were detected with abnormal liver function. Increased BMI, length of service, DBP, SBP, and being male were independent risk factors for abnormal liver function. The risk of abnormal liver function rises sharply with increasing length of service below 10 years. AUC values of the model were 0.764 (95% CI: 0.746-0.783) and 0.756 (95% CI: 0.727-0.786) in the training and test sets, respectively. The other four evaluation indices of the DNN model also achieved good values

Automotive industry · Liver function · Liver function tests · Logistic regression · Statistics · Engineering · Mathematics · Medicine · Occupational Health and Safety Research · Internal Medicine

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